Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A system, comprising one or more server hardware computing devices communicatively coupled to a network, the one or more server hardware computing devices comprising one or more processors and memory storing: first decision logic executable by the one or more processors to produce, based on data associated with a plurality of candidate risk factors, a candidate trust score within a predefined range; and specific computer-executable instructions that, when executed by the one or more processors, cause the system to: receive a first message associated with a first user account of an online proctored examination platform, wherein the online proctored examination platform provides monitoring of examinations using a set of computing resources; determine that the first message includes a request to perform a first risk analysis of a candidate associated with the first user account, the first risk analysis describing a risk of cheating or theft posed by the candidate to the online proctored examination platform; identify, in the plurality of candidate risk factors, one or more internal factors and one or more external factors; obtain, from one or more internal data stores of the online proctored examination platform, internal data associated with the one or more internal factors; obtain, from one or more external data sources that are external to the online proctored examination platform, external data associated with the candidate and with the one or more external factors; execute the first decision logic, using the internal data and the external data as the data associated with the plurality of candidate risk factors, to produce a first trust score within the predefined range, the first trust score representing the risk posed by the candidate; associate the first trust score with the first user account; identify a set of computing resources based on the first trust score; configure the online proctored examination platform to utilize the set of computing resources to monitor an examination process of a user associated with the first user account and perform a first action based on the first trust score.
The system is designed for online proctored examination platforms to assess and mitigate risks of cheating or theft by candidates. The system includes server hardware computing devices connected to a network, equipped with processors and memory storing decision logic and executable instructions. The decision logic generates a trust score within a predefined range based on candidate risk factors. The system receives a message from a user account on the online proctored examination platform, which requests a risk analysis of the candidate. The system identifies internal and external risk factors, retrieves internal data from the platform's data stores, and obtains external data from sources outside the platform. Using this data, the system executes the decision logic to produce a trust score representing the candidate's risk level. This score is associated with the user account. Based on the trust score, the system identifies and configures a set of computing resources to monitor the candidate's examination process and performs an action based on the score. The system dynamically adjusts monitoring and resource allocation to enhance security and integrity in online proctored examinations.
2. The system of claim 1 , wherein the one or more server hardware computing devices are in communication, via the network, with a first client device used by the candidate to perform the examination process on the online proctored examination platform, and the instructions, when executed by the one or more processors, further cause the system to: receive user input from the first client device in each of a plurality of phases of the examination process, the plurality of phases including: a registration phase that includes validating the candidate's identity and testing environment; a greeting phase that includes matching the candidate to a proctor and establishing at least one of a video data connection and an audio data connection between the first client device and a second client device used by the proctor to access the online proctored examination platform; and an examination phase that includes administering an examination and facilitating the proctor to monitor the candidate during the examination; and parse the user input based on the one or more internal factors to produce real-time data associated with the one or more internal factors, the internal data comprising the real-time data.
The system relates to online proctored examination platforms designed to securely administer and monitor remote examinations. The problem addressed is ensuring the integrity of online exams by validating candidate identity, verifying testing environments, and enabling real-time proctoring to prevent cheating. The system includes server hardware computing devices that communicate with client devices used by candidates and proctors. During the examination process, the system handles multiple phases: registration, greeting, and examination. In the registration phase, the system validates the candidate's identity and tests their environment to ensure compliance with exam requirements. In the greeting phase, the system matches the candidate with a proctor and establishes video and audio connections between the candidate's device and the proctor's device. In the examination phase, the system administers the exam while allowing the proctor to monitor the candidate in real time. The system processes user input from the candidate's device across these phases, analyzing it based on internal factors to generate real-time data. This data helps detect anomalies or suspicious behavior during the exam, enhancing security and fairness. The system ensures seamless communication and monitoring throughout the examination process, reducing the risk of fraud or unauthorized assistance.
3. The system of claim 1 , wherein to perform the first action, the instructions, when executed by the one or more processors, cause the system to: associate, with the predefined range for the candidate trust score, one or more examination parameters that configure an examination requested by the candidate, so that the one or more examination parameters are modified to provide an increasing level of monitoring of the candidate during the examination as the risk posed by the candidate, indicated by the candidate trust score, increases; determine, based on the first trust score, corresponding values of the examination parameters; and cause the examination to be configured using the corresponding values of the examination parameters.
This invention relates to a system for dynamically adjusting examination parameters based on a candidate's trust score to enhance monitoring and security during assessments. The system evaluates a candidate's trust score, which indicates their risk level, and associates this score with a predefined range to determine appropriate examination parameters. As the candidate's risk increases, the system modifies these parameters to escalate monitoring intensity. For example, higher-risk candidates may face stricter supervision, additional verification steps, or more frequent checks. The system calculates specific parameter values based on the candidate's trust score and applies these values to configure the examination accordingly. This approach ensures that monitoring efforts are proportionate to the perceived risk, balancing security with candidate experience. The invention is particularly useful in high-stakes assessments where fraud prevention and integrity are critical, such as academic, professional, or certification exams. By dynamically adjusting parameters, the system reduces administrative overhead while maintaining robust security measures.
4. The system of claim 1 , wherein the memory further stores second decision logic executable by the one or more processors to produce a proctor trust score based on data associated with a plurality of proctor risk factors, and the instructions, when executed by the one or more processors, further cause the system to: receive a second message associated with a second user account of an online proctored examination platform; determine that the second message includes a request to perform a second risk analysis of a proctor associated with the second user account, the second risk analysis describing a fraud risk posed by the proctor to the online proctored examination platform; obtain, from the one or more internal data stores, historical data associated with the second user account and with the plurality of proctor risk factors; execute the second decision logic, using the historical as the data associated with the plurality of proctor risk factors, to produce a second trust score representing the fraud risk posed by the proctor; associate the second trust score with the second user account; and perform a second action based on the second trust score.
This invention relates to a system for assessing fraud risk in online proctored examinations by evaluating proctor trustworthiness. The system addresses the problem of ensuring integrity in online exams by identifying potentially fraudulent proctors who may compromise test security. The system includes a memory storing decision logic that generates a trust score based on multiple proctor risk factors, such as historical behavior, authentication data, and other relevant metrics. When a request is received to analyze a proctor associated with a user account, the system retrieves historical data linked to that account and the risk factors. The decision logic processes this data to produce a trust score indicating the proctor's fraud risk. This score is then linked to the user account, and the system performs an action based on the score, such as flagging suspicious activity, restricting access, or triggering further verification. The system enhances exam security by dynamically assessing proctor reliability and mitigating fraud risks in real time.
5. A system, comprising one or more server hardware computing devices communicatively coupled to a network, the one or more server hardware computing devices comprising one or more processors and memory storing specific computer-executable instructions that, when executed by the one or more processors, cause the system to: receive a first request to calculate a trust score for a user associated with a user account of an online proctored examination platform, the trust score representing results of a risk analysis of the user, wherein the online proctored examination platform provides monitoring of examinations using a set of computing resources; obtain internal data of the online proctored examination platform, the internal data describing usage of the online proctored examination platform by the user; perform the risk analysis using at least the internal data to produce the trust score; associate the trust score with the user account; and perform an action based on the trust score, wherein the action includes: identifying a set of computing resources based on the first trust score; configuring the online proctored examination platform to utilize the set of computing resources to monitor an examination process of a user associated with the first user account.
The system involves a server-based platform designed to assess user trustworthiness in online proctored examinations. The technology addresses the challenge of ensuring exam integrity by dynamically evaluating user risk and allocating appropriate monitoring resources. The system receives a request to calculate a trust score for a user, which quantifies their risk level based on internal platform data, such as usage patterns and behavior. This data is analyzed to generate a trust score, which is then linked to the user's account. The system uses this score to determine the necessary computing resources for monitoring the user's examination, ensuring efficient and tailored oversight. Higher-risk users may trigger more intensive monitoring, while lower-risk users may receive lighter scrutiny, optimizing resource allocation and exam security. The platform dynamically adjusts monitoring based on real-time risk assessments, enhancing both security and operational efficiency. This approach helps prevent cheating while minimizing unnecessary resource consumption.
6. The system of claim 5 , wherein the internal data comprises information describing user input collected by the online proctored examination platform from a client device used by the user to access the online proctored examination platform and to participate in the examination process.
This invention relates to online proctored examination systems, specifically focusing on the collection and processing of user input data during an examination. The system addresses challenges in ensuring exam integrity by monitoring and analyzing user interactions within an online proctored environment. The system captures and stores internal data, which includes detailed information about user input collected from a client device used by the examinee to access the online proctored examination platform. This data encompasses various forms of user interactions, such as keystrokes, mouse movements, screen activity, and other input events generated during the examination process. By recording and analyzing this input data, the system can detect anomalies, suspicious behavior, or deviations from expected patterns, thereby enhancing the ability to maintain exam security and prevent cheating. The system may also integrate with other monitoring components, such as video or audio surveillance, to provide a comprehensive assessment of user behavior during the examination. The collected data can be used for real-time or post-examination review to verify the legitimacy of the exam session and ensure compliance with proctoring protocols. This approach improves the reliability and trustworthiness of online proctored examinations by leveraging detailed user input data to enforce exam integrity.
7. The system of claim 6 , wherein the examination process comprises a plurality of phases including an examination phase wherein an examination is administered, the user input comprises video data captured by an imaging device of the client device during one or more pre-examination phases, of the plurality of phases, occurring before the examination phase, and the instructions, when executed by the one or more processors, cause the system to: receive, as the internal data, the information describing the user input; and to perform the risk analysis: determine that the information describing the user input includes a testing environment alert generated by the online proctored examination platform in response to a determination that the video data depicts a security violation in a testing environment of the user; and calculate the trust score based at least in part on an identification of high-risk behavior of the user indicated by the testing environment alert.
This invention relates to an online proctored examination system designed to detect and assess security violations during remote testing. The system monitors user behavior before and during an examination to identify potential cheating or unauthorized activities. During pre-examination phases, video data is captured by the user's device and analyzed for security breaches, such as unauthorized materials, suspicious movements, or environmental anomalies. The system generates testing environment alerts when high-risk behavior is detected, which are then used to calculate a trust score reflecting the user's integrity. This score helps determine the validity of the examination results. The system integrates these alerts into a broader risk analysis framework, ensuring secure and reliable remote testing. The invention enhances proctoring accuracy by leveraging automated video analysis to flag potential violations before or during the exam, reducing the need for manual oversight. The trust score provides a quantifiable measure of user compliance, aiding in fraud detection and maintaining examination integrity.
8. The system of claim 6 , wherein the examination process comprises a plurality of phases including an examination phase wherein an examination is administered, the user input comprises video data captured by an imaging device of the client device during the examination phase, and the instructions, when executed by the one or more processors, cause the system to: receive, as the internal data, the information describing the user input; and to perform the risk analysis: determine that the information describing the user input includes a behavior alert generated by the online proctored examination platform in response to a determination that the video data depicts one or both of abnormal behavior and high-risk behavior of the user; and calculate the trust score based at least in part on an identification of high-risk behavior of the user indicated by the behavior alert.
This invention relates to online proctored examination systems designed to detect and assess suspicious behavior during remote testing. The system monitors users via video data captured by their client devices during an examination phase to identify abnormal or high-risk behavior. The system processes this video data to generate behavior alerts when suspicious activity is detected, such as unauthorized movements, distractions, or other irregularities. These alerts are used to calculate a trust score, which quantifies the likelihood of cheating or other security risks. The system integrates these alerts into a broader risk analysis framework, allowing administrators to evaluate the integrity of the examination process. The invention aims to enhance the reliability of remote proctoring by automating the detection of high-risk behavior and providing a measurable trust score to assess examination validity. This approach helps mitigate cheating in online assessments while reducing the need for manual oversight.
9. The system of claim 6 , wherein the internal data further comprises one or more device attributes of the client device and the instructions, when executed by the one or more processors, further cause the system to: receive the one or more device attributes; and to perform the risk analysis: obtain a set of minimum device requirements; compare the one or more device attributes to the set of minimum device requirements to produce a device security evaluation; and calculate the trust score based at least in part on the device security evaluation, wherein the trust test score indicates an increased risk when the one or more device attributes fall below the set of minimum device requirements.
A system for evaluating device security and calculating a trust score for a client device in a networked environment. The system addresses the challenge of assessing the security posture of client devices to mitigate risks associated with compromised or non-compliant devices. The system includes one or more processors and memory storing instructions that, when executed, perform a risk analysis by obtaining device attributes from the client device. These attributes are compared against a predefined set of minimum device requirements, such as software versions, security patches, or hardware capabilities, to generate a device security evaluation. The evaluation determines whether the device meets the required security standards. The system then calculates a trust score based on this evaluation, where a lower score indicates higher risk if the device attributes fall below the minimum requirements. This trust score can be used to enforce access controls, prioritize security actions, or trigger remediation steps. The system ensures that only devices meeting security standards are granted appropriate levels of access, reducing the risk of unauthorized or vulnerable devices compromising the network.
10. The system of claim 6 , wherein the instructions, when executed by the one or more processors, further cause the system to: receive, during the examination process, the information describing the user input substantially in real-time as the user input is collected by the online proctored examination platform; and perform the risk analysis to update the trust score substantially concurrently with receipt of the information describing the user input.
The system relates to online proctored examination platforms designed to detect and mitigate cheating or unauthorized behavior during remote assessments. Traditional online exams lack real-time monitoring capabilities, making it difficult to promptly identify suspicious activities such as unauthorized assistance or rule violations. This system addresses the problem by providing continuous, real-time analysis of user input during an examination to dynamically assess trustworthiness. The system includes one or more processors and memory storing instructions that, when executed, enable real-time monitoring and risk assessment. During an exam, the system receives user input data—such as keystrokes, mouse movements, or other interactions—as it is collected by the platform. This input is analyzed in real-time to evaluate potential risks, such as unusual patterns or deviations from expected behavior. The system then updates a trust score dynamically, reflecting the current level of confidence in the exam's integrity. This allows for immediate intervention if suspicious activity is detected, such as flagging the exam for review or triggering additional verification steps. The system may also integrate with other monitoring features, such as video or audio analysis, to provide a comprehensive assessment of exam integrity. By processing input data concurrently with its collection, the system ensures timely detection and response to potential cheating, enhancing the reliability of remote examinations.
11. The system of claim 5 , wherein the instructions, when executed by the one or more processors, cause the system to: receive, as the internal data, platform-generated data stored in one or more internal data stores of the online proctored examination platform, the platform-generated data describing past performance of one or more candidate users, including the user, on one or more examinations provided by the online proctored examination platform; and to perform the risk analysis: identify, in the platform-generated data, historical candidate data describing past performance of the user on the one or more examinations; and calculate the trust score based at least in part on the historical candidate data.
The system relates to online proctored examination platforms and addresses the challenge of assessing candidate trustworthiness during remote testing. The system analyzes internal platform data to evaluate user behavior and performance history, enhancing fraud detection and examination integrity. The system receives platform-generated data from internal data stores, which includes historical performance records of candidates, including the current user, across multiple examinations. During risk analysis, the system identifies the user's past performance data from this historical record. The trust score is then calculated based on this historical candidate data, allowing the platform to assess the user's reliability and potential risk of cheating or other misconduct. This approach leverages existing platform data to improve trust assessment without requiring additional external inputs, ensuring a more accurate and automated evaluation of candidate behavior. The system integrates seamlessly with the examination platform, enhancing security and reducing administrative overhead.
12. The system of claim 11 , wherein to perform the risk analysis, the instructions, when executed by the one or more processors, further cause the system to: determine from the historical candidate data a number of retaken examinations by the user; and calculate the trust score to represent a higher risk for each of the number of retaken examinations.
The invention relates to a system for assessing user trustworthiness based on historical examination data. The system addresses the problem of evaluating risk associated with users who have retaken examinations, which may indicate potential fraud or unreliable behavior. The system analyzes historical candidate data to determine the number of retaken examinations by a user and calculates a trust score that reflects a higher risk for each retaken examination. This trust score is used to assess the user's reliability, with more retakes leading to a lower trust score. The system may also compare the user's performance across multiple examinations to identify inconsistencies or patterns that further influence the trust score. By quantifying risk through examination retakes, the system helps organizations make informed decisions about user credibility, particularly in contexts where repeated attempts may signal dishonesty or incompetence. The system integrates this risk analysis into a broader trust assessment framework, ensuring that historical examination behavior is factored into overall trust evaluations.
13. The system of claim 11 , wherein to perform the risk analysis, the instructions, when executed by the one or more processors, further cause the system to: identify in the historical candidate data a first examination of the one or more examinations, and a first duration representing a length of time needed by the user to complete the first examination; determine, based on the platform-generated data, a median duration for all other candidate users to complete the first examination; compare the first duration to the median duration to produce a first deviation of the first duration from the median duration; determine whether the first duration exceeds one or both of a first threshold and a second threshold greater than the first threshold; responsive to a determination that the first duration does not exceed the first threshold, calculate the trust score to represent a lower risk; and responsive to a determination that the first duration exceeds the second threshold, calculate the trust score to represent a higher risk.
This invention relates to a system for assessing user risk in an examination or assessment platform by analyzing performance metrics. The system evaluates a user's examination completion time against historical data to determine trustworthiness. The system identifies a specific examination and the time taken by a user to complete it, then compares this duration to a median completion time derived from other users' performance data. The deviation from the median is calculated, and the system checks whether the user's time exceeds predefined thresholds. If the user's time is below the first threshold, the system assigns a lower-risk trust score, indicating higher trustworthiness. If the user's time exceeds the second, higher threshold, the system assigns a higher-risk trust score, indicating potential fraud or inefficiency. The system may also analyze multiple examinations and durations to refine risk assessment. This approach helps detect anomalies in user behavior, such as unusually fast or slow completion times, which may indicate cheating, automation, or other risks. The system supports adaptive trust scoring based on performance deviations from peer benchmarks.
14. The system of claim 5 , wherein to perform the risk analysis, the instructions, when executed by the one or more processors, cause the system to: obtain an existing trust score representing previous results of the risk analysis of the user; identify, in the internal data, usage data for evaluating a plurality of risk factors associated with the risk analysis, the usage data having not been used as a basis for determining the previous trust score information; determine, based on the usage data, one or more transformations to apply to the previous trust score, the one or more transformations describing a change in the risk posed by the user, the change resulting from the user's usage of the online proctored examination platform; and apply the one or more transformations to the previous trust score to produce the trust score.
The invention relates to an online proctored examination platform that performs risk analysis to assess user trustworthiness. The system addresses the challenge of dynamically updating trust scores based on new usage data while accounting for prior risk assessments. The system obtains an existing trust score derived from previous risk analysis of a user. It then identifies unused usage data from internal records, which pertains to multiple risk factors relevant to the risk analysis. The system analyzes this data to determine transformations that reflect changes in the user's risk profile due to their interactions with the platform. These transformations are applied to the existing trust score, generating an updated trust score that incorporates both historical and new risk factors. This approach ensures continuous evaluation of user behavior without requiring a full reassessment from scratch, improving efficiency and accuracy in trust scoring. The system dynamically adjusts risk assessments based on evolving user activity, enhancing the reliability of online proctoring.
15. The system of claim 5 , wherein the instructions, when executed by the one or more processors, further cause the system to: obtain, from one or more public data stores that are external to the online proctored examination platform, publicly available data associated with the user; and perform the risk analysis further using at least the publicly available data with the internal data to produce the trust score.
The system enhances online proctored examination security by analyzing user behavior and publicly available data to assess trustworthiness. The system monitors user actions during an exam, such as eye movements, head position, and environmental factors, to detect anomalies. It also collects internal data from the examination platform, including login patterns, device information, and historical performance. To further refine risk assessment, the system retrieves publicly available data from external sources, such as social media profiles, public records, or other open databases. This external data is combined with the internal monitoring data to generate a trust score, which quantifies the likelihood of fraudulent behavior. The system uses this score to flag suspicious activity, adjust exam conditions, or trigger additional verification steps. By integrating both internal and external data sources, the system improves the accuracy of fraud detection in online proctored exams, reducing the risk of cheating while maintaining a fair and secure testing environment.
16. The system of claim 5 , wherein the instructions, when executed by the one or more processors, further cause the system to: determine that the user has authorized the system to access, and has provided to the system user credentials to access, a user data provider that is external to the online proctored examination platform; connect to the user data provider over the network; obtain, from the user data provider using the credentials, user data associated with the user; and perform the risk analysis further using at least the user data with the internal data to produce the trust score.
An online proctored examination platform system enhances security by integrating external user data into risk analysis. The system connects to external user data providers, such as identity verification services or educational institutions, to retrieve additional user information. After obtaining user authorization and credentials, the system accesses these external sources over a network to gather relevant user data. This data is combined with internal platform data, such as user behavior, device information, or historical records, to perform a comprehensive risk analysis. The analysis generates a trust score, which evaluates the likelihood of fraudulent activity or unauthorized access. By incorporating external data, the system improves the accuracy and reliability of trust assessments, reducing the risk of cheating or identity fraud during online examinations. The integration ensures that the platform can verify user identities and behaviors more effectively, enhancing overall security and trust in the examination process.
17. The system of claim 16 , wherein the user data provider manages secured access to electronic education records, and the instructions, when executed by the one or more processors, further cause the system to: send to the user data provider a request to provide, from the electronic education records, course records in which identifying information of the user appears, the course records describing courses taken by the user and the user's performance in the courses; receive the course records as the user data; and to perform the risk analysis, for each of the courses described by the course records: determine a completion status of the course; responsive to a determination that the completion status indicates the user failed the course, modify the trust score by a first amount to represent an increase of the risk by a first degree; and responsive to a determination that the completion status indicates the user did not complete the course, modify the trust score by a second amount to represent an increase of the risk by a second degree.
A system for assessing risk based on educational records analyzes a user's academic history to determine trustworthiness. The system interfaces with a user data provider that securely manages access to electronic education records. The system requests and retrieves course records containing the user's identifying information, including details about courses taken and performance outcomes. During risk analysis, the system evaluates each course record to determine completion status. If a course was failed, the system increases a trust score by a first amount, indicating a higher risk level. If a course was not completed, the system increases the trust score by a second amount, representing a different risk level. The system adjusts the trust score based on these academic outcomes to quantify risk. This approach leverages educational performance as a factor in risk assessment, providing a data-driven method to evaluate user reliability. The system ensures secure access to sensitive educational data while processing it to derive risk insights.
18. The system of claim 16 , wherein the user data provider manages secured access to a social networking platform, the user having a user profile on the social networking platform, and the instructions, when executed by the one or more processors, further cause the system to obtain, as the user data, information from the user profile.
A system for managing user data access includes a user data provider that controls secured access to a social networking platform. The system retrieves user data from a user profile on the social networking platform. The system also includes a data requester that sends a request for user data, and a data access controller that determines whether the request complies with access rules. If the request complies, the data access controller provides the requested user data to the data requester. The system further includes a data access monitor that tracks data access events and generates reports. The user data provider ensures that only authorized users can access the social networking platform, and the system obtains information from the user profile as part of the user data. The system is designed to facilitate secure and compliant data sharing between different entities while maintaining control over user data access. The data access controller enforces access rules to prevent unauthorized data retrieval, and the data access monitor provides transparency and accountability in data access activities. The system is particularly useful in environments where user data privacy and security are critical, such as social networking platforms.
19. The system of claim 18 , wherein the user profile identifies, as a plurality of connections to the user, a plurality of social network users, and the instructions, when executed by the one or more processors, further cause the system to: obtain a corresponding current trust score associated with each of a plurality of candidate users of the online proctored examination platform; identify, as a plurality of high-risk candidate users, each of the plurality of candidate users wherein the corresponding current trust score indicates that the risk posed by the candidate user exceeds a predetermined threshold; obtain identifying information of each of the plurality of high-risk candidate users; communicate with the user data provider to receive, as the user data, a determination whether, based on the user profile and the identifying information, each of the plurality of high-risk candidate users is one of the plurality of connections to the user; and to perform the risk analysis: for each of the high-risk candidate users identified as one of the plurality of connections to the user, modify the trust score by a first amount to represent a higher risk; and responsive to the determination indicating that none of the plurality of high-risk candidate users are one of the plurality of connections to the user, modify the trust score to represent a lower risk.
This invention relates to online proctored examination platforms and addresses the challenge of accurately assessing the trustworthiness of users during remote testing. The system evaluates candidate users' risk levels based on trust scores, which are adjusted based on their social network connections. A user profile identifies a user's connections within a social network. The system obtains trust scores for candidate users and flags those exceeding a risk threshold as high-risk. For each high-risk candidate, the system checks if they are connected to the user via the social network. If a connection exists, the trust score is increased to reflect higher risk. If no connections are found, the trust score is decreased to indicate lower risk. This approach leverages social network data to refine risk assessments, improving the accuracy of trust scoring in online proctoring environments. The system dynamically adjusts risk evaluations based on social relationships, enhancing security and fairness in remote examinations.
20. A system, comprising one or more server hardware computing devices communicatively coupled to a network, the one or more server hardware computing devices comprising one or more processors and memory storing specific computer-executable instructions that, when executed by the one or more processors, cause the system to: receive a first request to calculate a trust score for a user associated with a user account of an online proctored examination platform, the trust score representing results of a risk analysis of the user, wherein the online proctored examination platform provides monitoring of examinations based on one or more monitoring parameters that control a plurality of monitoring functions; obtain internal data of the online proctored examination platform, the internal data describing usage of the online proctored examination platform by the user and including a current trust score for the user; perform the risk analysis, using at least the internal data and a plurality of risk factors for identifying high-risk behaviors and low-risk behaviors of the user, to produce an updated trust score, wherein the system updates the current trust score to represent a lower risk according to identified low-risk behaviors, and the system updates the current trust score to represent a higher risk according to identified high-risk behaviors; associate the updated trust score with the user account; and perform an action based on the updated trust score, by: selecting values for the one or more monitoring parameters based on the updated trust score, the values being selected to provide increasing monitoring as the risk represented by the updated trust score increases, wherein one or more of the monitoring functions require an allocation of computing resources of the online proctored examination platform, the one or more monitoring parameters include one or more resource parameters indicating an amount and configuration of the computing resources allocated to the one or more monitoring functions, and the system selects the corresponding values of the one or more resource parameters to cause the online proctored examination platform to modify the allocation and configuration of the computing resources to enhance the monitoring functions as the risk represented by the updated trust score increases.
The system involves a server-based platform for online proctored examinations that dynamically adjusts monitoring intensity based on user risk assessment. The platform evaluates user behavior to generate a trust score, which quantifies risk through analysis of internal usage data and predefined risk factors. High-risk behaviors increase the trust score, while low-risk behaviors decrease it. The system then associates this updated score with the user's account and adjusts monitoring parameters accordingly. Higher-risk users trigger more intensive monitoring, including increased allocation of computing resources to enhance surveillance functions. The platform optimizes resource allocation by dynamically configuring monitoring functions based on the user's risk profile, ensuring efficient use of computational resources while maintaining examination integrity. This adaptive approach balances security and resource utilization, tailoring monitoring to individual risk levels.
21. The system of claim 20 , wherein the action comprises the one or more processors executing the instructions to cause the system to: determine whether the risk represented by the updated trust score exceeds one or both of a first threshold and a second threshold higher than the first threshold; responsive to a determination that the risk posed by the user does not exceed the first threshold, store in the user account associated with the user a first setting indicating that the user is eligible to complete an examination provided by the online proctored examination platform using a client device of the user located in a testing environment of the user's choosing; and responsive to a determination that the risk posed by the user exceeds the second threshold, store in the user account associated with the user a second setting indicating that the user must complete the examination at a high-security in-person testing facility.
This invention relates to a risk-based system for determining examination eligibility in an online proctored examination platform. The system assesses user risk by evaluating an updated trust score, which reflects factors like behavior, device integrity, and historical data. The system compares this score against two thresholds: a lower first threshold and a higher second threshold. If the risk does not exceed the first threshold, the user is deemed eligible to take the examination remotely using their own client device in a self-selected testing environment. If the risk exceeds the second threshold, the user is required to take the examination at a high-security in-person testing facility. The system dynamically adjusts examination requirements based on risk levels, balancing security and accessibility. The trust score is continuously updated, allowing the system to adapt to changes in user behavior or system conditions. This approach ensures higher security for high-risk users while minimizing unnecessary restrictions for low-risk users. The system integrates with user accounts to store eligibility settings, streamlining the examination process based on risk assessment.
22. The system of claim 20 , wherein the online proctored examination platform provides examinations to candidate users via an examination process comprising one or more phases for setting up the examination and an examination phase for conducting the examination, the one or more phases for setting up the examination include a plurality of security procedures, and the action comprises the one or more processors executing the instructions to cause the system to: determine whether the risk represented by the updated trust score exceeds a threshold; and responsive to a determination that the risk posed by the user does not exceed the threshold, store in the user account associated with the user a setting indicating that the user is eligible to elect to skip one or more of the plurality of security procedures.
The system relates to online proctored examination platforms designed to securely administer exams to remote candidates. A key challenge in such platforms is balancing security with user convenience, as excessive security measures can deter candidates while insufficient measures risk exam integrity. The system addresses this by dynamically adjusting security procedures based on a user's trust score, which reflects their historical behavior and risk profile. During the examination process, which includes setup phases and an active exam phase, the system evaluates whether a user's updated trust score exceeds a predefined risk threshold. If the user's risk is below the threshold, the system grants them the option to skip one or more security procedures, such as identity verification or environment checks, during subsequent exam setups. This adaptive approach reduces friction for low-risk users while maintaining robust security for higher-risk individuals. The trust score is continuously updated based on user behavior, ensuring the system remains responsive to changes in risk levels. This method enhances user experience for trusted candidates while preserving the integrity of the examination process.
23. The system of claim 20 , wherein a first monitoring parameter of the one or more monitoring parameters indicates how many proctors to assign to monitor an examination of the user, the corresponding value of the first monitoring parameter being an integer that the system increments toward a maximum number of proctors as the risk represented by the updated trust score increases.
This invention relates to an automated proctoring system for monitoring examinations, addressing the challenge of dynamically adjusting the level of supervision based on a user's risk profile. The system assesses a user's trust score, which quantifies the likelihood of cheating or unauthorized behavior during an exam. As the trust score increases, indicating higher risk, the system automatically assigns more proctors to monitor the user's examination. The number of proctors is determined by a monitoring parameter, which is an integer value that increments toward a predefined maximum as the risk level rises. This ensures that higher-risk users receive more intensive oversight, while lower-risk users are monitored with fewer resources. The system may also incorporate additional monitoring parameters to further refine the proctoring strategy, such as adjusting the frequency or type of monitoring based on the user's behavior. The dynamic allocation of proctors optimizes resource usage by balancing security and efficiency, reducing the need for constant manual oversight while maintaining exam integrity. The invention is particularly useful in online education and certification environments where remote proctoring is required.
24. The system of claim 20 , wherein the action comprises the one or more processors executing the instructions to cause the system to: determine that the first request is associated with an examination process of the online proctored examination platform, the examination process initiated by the user; determine whether the updated trust score is within a first range associated with high-risk behavior; responsive to a determination that the updated trust score is within the first range, encode into a first array defining a first queue a reference identifying the user; responsive to a determination that the updated trust score is outside of the first range, encode the reference into a second array defining a second queue; receive an indication that the reference is at a front of the first queue or the second queue; and responsive to the indication: if the reference is in the first queue, cause a first proctor and a second proctor to be selected from a first pool of proctors and assigned to the examination process, the first pool of proctors being associated with high-risk candidates; and if the reference is in the second queue, cause the first proctor to be selected from a second pool of proctors and assigned to the examination process.
This invention relates to an online proctored examination system that dynamically assigns proctors based on a user's trust score, which assesses risk behavior during an examination. The system monitors user activity and updates a trust score to determine if the user exhibits high-risk behavior. When a user initiates an examination, the system checks whether their updated trust score falls within a predefined high-risk range. If the score is within this range, the user's reference is added to a high-risk queue, triggering the selection and assignment of two proctors from a specialized pool designated for high-risk candidates. If the score is outside the high-risk range, the user's reference is placed in a standard queue, and a single proctor is assigned from a general pool. The system processes the queues sequentially, assigning proctors as users reach the front of their respective queues. This approach ensures that high-risk candidates receive additional oversight while optimizing proctor resources for lower-risk examinations. The invention improves examination integrity by dynamically adjusting proctoring levels based on real-time risk assessments.
25. The system of claim 20 , wherein the action comprises the one or more processors executing the instructions to cause the system to: determine whether the updated trust score is within a first range associated with high risk behavior; responsive to a determination that the updated trust score is within the first range, encode into a first array defining a first queue a reference identifying the user; responsive to a determination that the updated trust score is outside of the first range, encode the reference into a second array defining a second queue; identify a candidate user requesting an examination process of the online proctored examination platform; determine that a corresponding user account of the candidate user includes a setting excluding proctors having a corresponding trust score within the first range; obtain an identifier at a front of the second queue; determine that the identifier is associated with a first proctor; and cause the first proctor to be assigned to the examination process.
This invention relates to an online proctored examination system that dynamically assigns proctors based on trust scores to mitigate high-risk behavior. The system evaluates user trust scores to categorize users into different queues based on risk levels. If a user's trust score falls within a predefined high-risk range, their identifier is added to a first queue. Otherwise, it is added to a second queue. When a candidate user requests an examination, the system checks if their account settings exclude proctors with high-risk trust scores. If so, the system selects a proctor from the second queue, ensuring the assigned proctor does not have a high-risk trust score. This approach enhances security by dynamically matching proctors to candidates based on risk assessments, reducing the likelihood of fraudulent or compromised proctoring. The system automates the assignment process, improving efficiency and reliability in online examination environments. The trust score evaluation and queue-based selection ensure that proctors are appropriately matched to candidates, maintaining the integrity of the examination process.
26. The system of claim 20 , wherein to obtain the internal data, the instructions, when executed by the one or more processors, cause the system to: determine whether the user is a candidate user that takes examinations provided by the online proctored examination platform, or a proctor user that proctors the examinations; responsive to a determination that the user is a candidate user, configure the internal data to include historical candidate data describing the candidate user's performance on completed examinations; and responsive to a determination that the user is a proctor user, configure the internal data to include historical proctor data describing the proctor user's performance while proctoring past examinations.
This invention relates to an online proctored examination system that customizes data collection and analysis based on user roles. The system identifies whether a user is a candidate (examinee) or a proctor (examination supervisor) and tailors the internal data it gathers accordingly. For candidates, the system collects historical performance data from completed examinations, including metrics like accuracy, completion time, and behavior patterns. For proctors, the system records performance data related to their supervision activities, such as monitoring efficiency, intervention frequency, and adherence to protocols. The system dynamically adjusts data collection to ensure relevant insights are captured for each role, improving personalized feedback and system optimization. This approach enhances examination integrity by providing role-specific analytics that help candidates improve their test-taking strategies and proctors refine their oversight methods. The system ensures that data is contextually relevant, reducing noise and improving decision-making for both users and the platform.
27. The system of claim 20 , wherein the one or more server hardware computing devices are in communication, via the network, with a third party system authorized to receive completed examination data associated with the user, and the action comprises the one or more processors executing the instructions to cause the system to: determine that the first request is associated with a completed examination; determine whether the risk represented by the updated trust score exceeds one or both of a first threshold and a second threshold higher than the first threshold; responsive to a determination that the risk does not exceed the first threshold, compile examination results of the completed examination to produce the completed examination data in a format readable by the third party system; responsive to a determination that the risk exceeds the first threshold and does not exceed the second threshold, compile the examination results with the updated trust score to produce the completed examination data in the format; responsive to a determination that the risk exceeds the second threshold: obtain collected data captured by the online proctored examination platform during an examination process of the examination; and compile the examination results with the updated trust score and the collected data to produce the completed examination data in the format; and send the completed examination data to the third party system.
The system involves a secure online proctored examination platform that evaluates user trustworthiness and adjusts data sharing based on risk levels. The platform uses server hardware computing devices connected to a network and a third-party system authorized to receive examination data. During an examination, the system monitors user behavior and generates an updated trust score representing the risk of cheating or unauthorized activity. After the examination, the system determines whether the risk exceeds predefined thresholds. If the risk is below a first threshold, the system compiles examination results in a format compatible with the third-party system. If the risk exceeds the first but not a higher second threshold, the system includes the trust score with the results. If the risk exceeds the second threshold, the system also incorporates collected data from the examination process, such as video, audio, or behavioral analytics, before sending the compiled data to the third-party system. This ensures that higher-risk examinations are flagged with additional evidence for review. The system automates risk-based data sharing to enhance examination integrity and provide third-party systems with contextually relevant information.
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November 24, 2020
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